Update app.py

app.py

@@ -227,13 +227,15 @@ if 'preprocessor' not in st.session_state:
 
 # Sidebar Navigation
 st.sidebar.title("🔮 Data Wizard Pro")
+# Replace the existing app_mode section with this:
 app_mode = st.sidebar.radio("Navigate", [
     "Data Upload",
     "Smart Cleaning",
     "Advanced EDA",
     "Model Training",
     "Predictions",
-    "Visualization Lab"
+    "Visualization Lab",
+    "Neural Network Studio"  # New option
 ])
 
 # --- Progress Bar ----
@@ -366,19 +368,46 @@ elif app_mode == "Smart Cleaning":
             missing_strategy_num = "Median"
             missing_strategy_cat = "Most Frequent"
 
-            if clean_action == "Handle Missing Values":
-                st.markdown("**Configure how missing values will be handled.**")
-                all_impute_cols = ["All Columns"] + df.columns.tolist()
-                impute_cols = st.multiselect("Columns to Impute", all_impute_cols, default=["All Columns"], help="Select the columns with missing values to impute. Choose 'All Columns' to apply to all columns with missing values.")
-                if "All Columns" in impute_cols:
-                    impute_cols = df.columns.tolist()
-
-                method = st.selectbox("Imputation Method", [
-                    "KNN Imputation",
-                    "Median Fill",
-                    "Mean Fill",
-                    "Drop Missing"
-                ], help="Choose the method to use for imputing missing values.")
+          elif clean_action == "Handle Missing Values":
+        st.markdown("**Configure how missing values will be handled.**", unsafe_allow_html=True)
+        all_impute_cols = ["All Columns"] + df.columns.tolist()
+        impute_cols = st.multiselect("Columns to Impute", all_impute_cols, default=["All Columns"], help="Select the columns with missing values to impute. Choose 'All Columns' to apply to all columns with missing values.")
+        if "All Columns" in impute_cols:
+            impute_cols = df.columns.tolist()
+
+        method = st.selectbox("Imputation Method", [
+            "KNN Imputation",
+            "Median Fill",
+            "Mean Fill",
+            "Drop Missing"
+        ], help="Choose the method to use for imputing missing values.")
+
+    elif clean_action == "Neural Network Prep":
+        st.markdown("**Neural Network Specific Preparation**", unsafe_allow_html=True)
+        
+        # Make dynamic to check if the models can allow it
+        validModels=["RNN, CNN"]
+
+        model_Choice_text = st.radio("What's a use case for Models?",
+         options= validModels)
+         y/ st.toggle
+        
+        display a string or some other feedback
+             st.info('Select a machine learning task below!')
+
+      ## to check which text based mode
+
+        validColumnNumerical_cols = df.select_dtypes(include=['int','float']).columns.tolist()
+        numcol_cols = st.multiselect("Text use Colimns:  or sequence for model usage :D, to generate the code - to understand how each one plays out D", options =validColumnNumerical_cols )
+
+           #### Make different selections here now 
+               ####
+            st.code('Code example is generated.')
+ "" Make each configuration do an function or callback" just one press and more to learn""" ### Show code. You do need check what variables and show output that goes on
+  #### then, what did output happen if you pick A or B variable selection.
+
+        seq_length = st.number_input("Sequence Length (for RNN)", 10, 100, 30, help =" Length to do that. make them more power ")
+
                 if method == "KNN Imputation":
                     knn_neighbors = st.slider("KNN Neighbors", 2, 10, 5, help="Number of neighbors for KNN Imputation.") #Parameter
 
@@ -959,4 +988,98 @@ elif app_mode == "Visualization Lab":
 
 
         except Exception as e:
-            st.error(f"Error generating visualization: {e}")
+            st.error(f"Error generating visualization: {e}")
+
+elif app_mode == "Neural Network Studio":
+    st.title("🧠 Deep Learning Studio")
+    
+    # Network Type Selection
+    nn_type = st.selectbox("Network Type", [
+        "Multilayer Perceptron (MLP)",
+        "Convolutional Neural Network (CNN)",
+        "Recurrent Neural Network (RNN/LSTM)",
+        "Transformer",
+        "Autoencoder"
+    ])
+    
+    # Dynamic Configuration
+    with st.expander("Architecture Configuration"):
+        if nn_type == "MLP":
+            num_layers = st.slider("Hidden Layers", 1, 10, 3)
+            activation = st.selectbox("Activation Function", ["relu", "sigmoid", "tanh"])
+            
+        elif nn_type == "CNN":
+            conv_layers = st.slider("Convolutional Layers", 1, 5, 2)
+            filters = st.number_input("Filters per Layer", 32, 512, 64)
+            
+        elif nn_type == "RNN/LSTM":
+            rnn_units = st.number_input("LSTM Units", 64, 1024, 128)
+            return_sequences = st.checkbox("Return Sequences")
+            
+    # Training Configuration
+    with st.expander("Training Configuration"):
+        batch_size = st.selectbox("Batch Size", [32, 64, 128, 256], index=1)
+        epochs = st.slider("Epochs", 1, 100, 20)
+        learning_rate = st.number_input("Learning Rate", 1e-5, 1e-1, 1e-3)
+        optimizer = st.selectbox("Optimizer", ["Adam", "SGD", "RMSprop"])
+
+        # Place this after the Training Configuration expander
+        if st.button("Start Training"):
+            loss_placeholder = st.empty()
+            loss_history = []
+    
+        # Simulated training loop
+        for epoch in range(epochs):
+        # Simulate training progress
+        loss = np.random.rand()  # Replace with actual training logic
+        loss_history.append(loss)
+        
+        # Update plot
+        plt.figure()
+        plt.plot(loss_history, label="Training Loss")
+        plt.xlabel("Epoch")
+        plt.ylabel("Loss")
+        plt.legend()
+        loss_placeholder.pyplot(plt)
+        plt.close()
+        
+        st.write(f"Epoch {epoch+1}/{epochs} - Loss: {loss:.4f}")
+    
+    st.success("Training complete!")
+    
+    # Educational Content
+    with st.expander("📚 Network Fundamentals"):
+        st.markdown("""
+        **MLP Fundamentals**
+        - Basic feedforward architecture
+        - Ideal for tabular data
+        - Activation functions guide
+        """)
+        
+        if nn_type == "CNN":
+            st.markdown("""
+            **CNN Concepts**
+            - Convolutional filters
+            - Pooling operations
+            - Feature hierarchy
+            """)
+
+        if st.checkbox("Enable Model Interpretation"):
+    interpretation_type = st.selectbox("Interpretation Method", [
+        "SHAP Values",
+        "LIME",
+        "Attention Visualization"
+    ])
+    
+    if interpretation_type == "SHAP Values":
+        st.markdown("""
+        **SHAP (SHapley Additive exPlanations)**
+        - Explains individual predictions
+        - Shows feature importance
+        """)
+        
+        if st.button("Generate SHAP Values"):
+            with st.spinner("Calculating SHAP values..."):
+                # Add SHAP calculation logic here
+                st.success("SHAP values calculated!")
+                st.image("shap_plot.png")  # Replace with actual SHAP plot